4.6 Article

Measurement of Soy Contents in Ground Beef Using Near-Infrared Spectroscopy

期刊

APPLIED SCIENCES-BASEL
卷 7, 期 1, 页码 -

出版社

MDPI
DOI: 10.3390/app7010097

关键词

PLS; SVM; quantification; classification; dispersive NIR; FT-NIR

资金

  1. China National Science and Technology Support Program [2012BAK08B04]

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Models for determining contents of soy products in ground beef were developed using near-infrared (NIR) spectroscopy. Samples were prepared by mixing four kinds of soybean protein products (Arconet, toasted soy grits, Profam and textured vegetable protein (TVP)) with ground beef (content from 0%-100%). NIR spectra of meat mixtures were measured with dispersive (400-2500 nm) and Fourier transform NIR (FT-NIR) spectrometers (1000-2500 nm). Partial least squares (PLS) regression with full leave-one-out cross-validation was used to build prediction models. The results based on dispersive NIR spectra revealed that the coefficient of determination for cross-validation (R-cv(2)) ranged from 0.91 for toasted soy grits to 0.99 for Arconet. The results based on FT-NIR spectra exhibited the best prediction for toasted soy grits (R-cv(2) = 0.99) and R-cv(2) > 0.98 for the other three soy types. For identification of different types of soy products, support vector machine (SVM) classification was used and the total accuracy for dispersive NIR and FT-NIR was 95% and 83.33%, respectively. These results suggest that either dispersive NIR or FT-NIR spectroscopy could be used to predict the content and the discrimination of different soy products added in ground beef products. In application, FT-NIR spectroscopy methods would be recommended if time is a consideration in practice.

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